---
license: apache-2.0
base_model: JackFram/llama-160m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llama-160m-boolq
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# llama-160m-boolq

This model is a fine-tuned version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6795
- Accuracy: 0.5957

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.99  | 73   | 0.6870          | 0.5731   |
| No log        | 1.99  | 147  | 0.6825          | 0.5957   |
| No log        | 3.0   | 221  | 0.6809          | 0.6012   |
| No log        | 3.96  | 292  | 0.6795          | 0.5957   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3